Concluding Remarks

Spatial Data Programming with R

Bogdan G. Popescu
bogdan.popescu@johncabot.edu

John Cabot University

Learning Outcomes: Overview

Some of the learning outcomes in this course focused on:

  • executing basic programming tasks in R (e.g. loops, conditional statements, while statements, etc.)
  • understanding basic GIS terms and concepts
  • utilizing GIS for conducting spatial analyses.
  • appreciating the design and structure of a geographic information system (GIS) as a decision-making tool.
  • producing maps

Skills Acquired

  • clean and process data
  • visualize data
  • create interactive web-apps
  • typeset: write visually appealing articles and presentations (R Quarto)

Jobs where these skills are valued

  • Data Scientist/Data Analyst

  • GIS Analyst/GIS Specialist

  • Environmental Scientist

  • Market Research Analyst

  • Remote Sensing Specialist

  • Transportation Planner

Use of R

R is used in a variety of fields:

  • Finance
  • Academic research
  • Government
  • Retail
  • Data Journalism
  • Healthcare

Companies that use R

Examples of companies which use R include

  • Airbnb
  • Microsoft
  • Uber
  • Facebook
  • Google

Additional Good resources for learning R

  • R for Data Science
    http://r4ds.had.co.nz/
    Introduction to data analysis using R, focused on the tidyverse packages
    Good substitute for Stata

Good resources for learning R

Books to Use: Data Analysis and Visualization

Modern Statistics with R

R for Data Science

ggplot2

Books to Use: GIS

Geocomputation with R

Spatial Data Science

Other useful Sources

Overview of Processing Vector Layers

sf was the main library that we worked with

It helped us deal with:

  • Numerical Operations to calculate: Areas, Length, Distances, etc.
  • GIS Logical Operations: Overlaps, Equals, Intersects, etc.
  • Geometry Operations: Centroid, Buffer, Intersection, Union, Difference, etc.

Overview of Processing Raster Layers

We performed geometric operation on rasters (pictures) with the stars package:

  • Accessing cell values - as a matrix or as a dataframe, extracting pixels to points
  • Performing Raster algebra: raster arirthmentic and logic
  • Changing the resolution and extent: cropping, mosaicing, resampling, and reprojecting
  • Transforming Rasters: to points and polygons

Processing Raster Layers stars

Temperature in 1901

Processing Raster Layers stars

Temperature in 2022

Processing Raster Layers stars

Temperature difference between 2022 and 1901 > 4

Data visualization

  • ggplot2 is the library that allows to visualize data analysis results, but also to make maps
  • leaflet is a library that allows us to make interactive maps
  • mapview is a wrapper around leaflet automating the addition of: labels, popups, color scales, and common basemaps

Suppplmentary Lectures

I encourage you to check out the supplmentary lectures:

  • How to Make shapefiles using QGIS

  • How to Make a Website using Quarto and GitHub

Conclusion

Thank you and good luck!

Popescu (JCU): Concluding Remarks